An automatic voltage control (AVC) system is an important means to ensure a secure, economic and high-quality running of a power grid. A principle of the AVC lies in realizing a reasonable distribution of a reactive voltage in the power grid by coordinately controlling a reactive power output of a generator, a transformer tap, and a reactive power compensation apparatus, thus increasing a voltage stability margin, reducing an active power transmission loss, and improving a voltage eligibility rate, etc. A primary means for determining a coordinated controlling instruction is to solve an optimal power flow (OPF) model, as shown in a formula (1′):
                              min          ⁢                                          ⁢                      f            ⁡                          (                                                x                  0                                ,                                  u                  0                                            )                                      ⁢                                  ⁢                              s            .            t            .                                                  ⁢                                          g                0                            ⁡                              (                                                      x                    0                                    ,                                      u                    0                                                  )                                              =          0                ⁢                                  ⁢                              u            _                    ≤                      u            0                    ≤                      u            _                          ⁢                                  ⁢                              x            _                    ≤                      x            0                    ≤                      x            _                                              (                  1          ′                )            where u0 is a control variable vector, x0 is a state variable vector, an object function ƒ(x0,u0) is an active power transmission loss of the power system, a constraint equation g0(x0,u0)=0 is a power flow equation of the power system in a pre-contingency state, u is a lower limit of the control variable vector, ū is an upper limit of the control variable vector, x is a lower limit of the state variable vector, and x is an upper limit of the state variable vector.
With an increasing security requirement of a power grid operation, in an automatic voltage control process, a control result needs to satisfy a static security requirement, except that a base state security of the power system needs to be constrained. A security constrained optimal power flow (SCOPF) model is thus introduced to simultaneously take into account the security and economy of the power system, so as to generate an automatic voltage control instruction meeting the static security requirement.
Steps of a typical static security constrained automatic voltage control method may be illustrated as follows.
In step 1, a contingency set CAll scrutinized by a contingency assessment (CA) is set. The contingency assessment means that using a current result of the power flow as a base state of the power system, a result of the power flow after a contingency occurs in the power system is simulated, so as to predict whether a security risk exists in the power system and to determine whether a variable of the power system will be out of limit after a contingency occurs in the power system. A contingency is an outage of a component (such as a transmission line, a transformer, a generator, a load, a bus) of the power system and a combination thereof, which is predetermined in order to study an influence of the contingency on a secure of the power system. The contingency set CAll is represented by:CAll={contingency k|k=1, . . . ,NC},where NC is a number of the contingencies contained in the contingency set.
In step 2, power flow equations and variable constraints in all the post-contingency states (where the contingency is in the contingency set) are added to the optimal power flow model to construct a static security constrained optimal power flow model, as shown in a formula (2′):
                              min          ⁢                                          ⁢                      f            ⁡                          (                                                x                  0                                ,                                  u                  0                                            )                                      ⁢                                  ⁢                              s            .            t            .                                                  ⁢                                          g                0                            ⁡                              (                                                      x                    0                                    ,                                      u                    0                                                  )                                              =          0                ⁢                                  ⁢                                            g              k                        ⁡                          (                                                x                  k                                ,                                  u                  0                                            )                                =          0                ⁢                                  ⁢                              u            _                    ≤                      u            0                    ≤                                    u              _                        ⁢                                                  ⁢                          x              _                                ≤                      x            0                    ≤                      x            _                          ⁢                                  ⁢                                            x              _                        C                    ≤                      x            k                    ≤                                    x              _                        C                          ⁢                                  ⁢                              k            =            1                    ,          …          ⁢                                          ,                      N            C                                              (                  2          ′                )            where k is a series number of a power system state, k=0 represents the base state (or known as a pre-contingency state), k=1, . . . , NC represents a kth post-contingency state; u0 is a control variable vector, x0 is a state variable vector of the pre-contingency state, xk is a state variable vector of the kh post-contingency state, Nx is a number of elements contained in x0 or xk; a value of the control variable (such as a voltage amplitude of a generator bus) usually stays the same in the pre-contingency state and in the post-contingency states; a value of the state variable, which is usually different in the pre-contingency state and in the post-contingency states, is determined by a network structure of the power system, a parameter of an element, and the value of the control variable, such as a voltage amplitude of a load bus, a voltage amplitude of a contact bus, a reactive power output of a generator, and a voltage phase angle of each bus; an object function ƒ(x0,u0) is the active power transmission loss of the power system, a constraint equation g0(x0,u0)=0 is the power flow equation of the power system in the pre-contingency state, gk(xk,u0)=0 is a power flow equation of the power system in the kth post-contingency state; u is a lower limit of the control variable vector, ū is an upper limit of the control variable vector, x is a lower limit of the state variable vector in the pre-contingency state, x is an upper limit of the state variable vector in the pre-contingency state, xC is a lower limit of the state variable vectors in the post-contingency states, and xC is an upper limit of the state variable vectors in the post-contingency states.
In step 3, the static security constrained optimal power flow model is solved to obtain the automatic voltage control instruction.
In step 4, an automatic voltage control is performed for the power system according to the automatic voltage control instruction.
For the typical static security constrained automatic voltage control method, because a number of the contingences set in the contingency set CAll is generally large so as to make an optimization model huge in scale, it is generally difficult to solve the optimization model in a practical conducting process of an automatic voltage control, and it is substantially impossible to solve the optimization model in a time required by an online conducting. Meanwhile, because of a strict post-contingency security constraint, it is possible to make a feasible region of the optimization model void, that is, there is no feasible solution, and thus a usable automatic voltage control instruction may not be obtained. Therefore, it is difficult for this typical static security constrained automatic voltage control method to meet a requirement of an online conducting of the automatic voltage control.